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DIGITAL TWIN
A digital twin is a virtual representation of an object, process or system that spans its lifecycle. It is updated from real-time data, and uses simulation and machine learning to help decision-making.
Our parsimonious neural network technology is an asset for digital twin applications:
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Predictive Maintenance: Early predication and detection of anomalies
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Evaluation and update of the model can be embedded on small chips
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Long term dynamic prediction
OUR SUCCESS STORIES
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NeurEco’s reinforcement learning optimizes antenna control, reducing interference and boosting reliability.
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NeurEco’s digital twin enables accurate gas network modeling, enhancing prediction, uptime, and efficiency.
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Renewable energy power production
NeurEco enables accurate 30-hour power forecasts for renewables, enhancing grid sales and maintenance planning.
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Predictive maintenance
NeurEco builds a digital twin to predict airplane wheel wear, optimizing maintenance and safety.
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NeurEco’s digital twin enables real-time control in embedded systems, enhancing precision and responsiveness.
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